1
|
Lujan G, Quigley JC, Hartman D, Parwani A, Roehmholdt B, Meter BV, Ardon O, Hanna MG, Kelly D, Sowards C, Montalto M, Bui M, Zarella MD, LaRosa V, Slootweg G, Retamero JA, Lloyd MC, Madory J, Bowman D. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. J Pathol Inform 2021; 12:17. [PMID: 34221633 PMCID: PMC8240548 DOI: 10.4103/jpi.jpi_67_20] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/20/2020] [Accepted: 11/02/2020] [Indexed: 12/13/2022] Open
Abstract
We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients.
Collapse
Affiliation(s)
- Giovanni Lujan
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | | | - Douglas Hartman
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Anil Parwani
- Department of Pathology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Brian Roehmholdt
- Department of Pathology, Southern California Permanente Medical Group, La Canada Flintridge, CA, USA
| | | | - Orly Ardon
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew G. Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Marilyn Bui
- Department of Anatomic Pathology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Mark D. Zarella
- Johns Hopkins Medicine Pathology Informatics, Baltimore, MD 21287, USA
| | - Victoria LaRosa
- Education Services Department, Oracle Corp, Austin, Texas, USA
| | | | | | | | - James Madory
- Department of Pathology, Medical University of South Carolina, Charleston, SC, USA
| | | |
Collapse
|
2
|
Abdelsamea MM, Grineviciute RB, Besusparis J, Cham S, Pitiot A, Laurinavicius A, Ilyas M. Tumour parcellation and quantification (TuPaQ): a tool for refining biomarker analysis through rapid and automated segmentation of tumour epithelium. Histopathology 2019; 74:1045-1054. [PMID: 30735268 DOI: 10.1111/his.13838] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 02/06/2019] [Indexed: 12/19/2022]
Abstract
BACKGROUND AND AIMS Immunohistochemistry (IHC) is an essential component of biomarker research in cancer. Automated biomarker quantification is hampered by the failure of computational algorithms to discriminate 'negative' tumour cells from 'negative' stromal cells. We sought to develop an algorithm for segmentation of tumour epithelium in colorectal cancer (CRC), irrespective of the biomarker expression in the cells. METHODS AND RESULTS We developed tumour parcellation and quantification (TuPaQ) to segment tumour epithelium and parcellate sections into 'epithelium' and 'non-epithelium'. TuPaQ comprises image pre-processing, extraction of regions of interest (ROIs) and quantification of tumour epithelium (total area occupied by epithelium and number of nuclei in the occupied area). A total of 286 TMA cores from CRC were manually annotated and analysed using the commercial halo software to provide ground truth. The performance of TuPaQ was evaluated against the ground truth using a variety of metrics. The image size of each core was 7000 × 7000 pixels and each core was analysed in a matter of seconds. Pixel × pixel analysis showed a sensitivity of 84% and specificity of 95% in detecting epithelium. The mean tumour area obtained by TuPaQ was very close to the area quantified after manual annotation (r = 0.956, P < 0.001). Moreover, quantification of tumour nuclei by TuPaQ correlated very strongly with that of halo (r = 0.891, P < 0.001). CONCLUSION TuPaQ is a very rapid and accurate method of separating the epithelial and stromal compartments of colorectal tumours. This will allow more accurate and objective analysis of immunohistochemistry.
Collapse
Affiliation(s)
- Mohammed M Abdelsamea
- School of Computer Science, University of Nottingham, Nottingham, UK.,Mathematics Department, University of Assiut, Assiut, Egypt
| | - Ruta B Grineviciute
- Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania
| | - Justinas Besusparis
- Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania
| | - Sulayman Cham
- Nottingham Molecular Pathology Node and Academic Unit of Pathology, Division of Cancer and Stem Cells, University of Nottingham, Queen's Medical Centre, Nottingham, UK
| | - Alain Pitiot
- Laboratory of Image and Data Analysis, Ilixa Ltd, London, UK
| | - Arvydas Laurinavicius
- Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania
| | - Mohammad Ilyas
- Nottingham Molecular Pathology Node and Academic Unit of Pathology, Division of Cancer and Stem Cells, University of Nottingham, Queen's Medical Centre, Nottingham, UK
| |
Collapse
|